launchdarkly-metric-create（启动 darkly metric create）

LaunchDarkly Metric Create

You're using a skill that will guide you through creating a LaunchDarkly metric. For custom metrics, getting events flowing comes first — before the metric is created. Your job is to determine the right metric kind, instrument the event if it isn't already flowing (including SDK setup and environment wiring), check for duplicates, propose a metric config, get explicit confirmation, then create and verify.

Prerequisites

This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.

Required MCP tools:

• create-metric — create the metric
• get-metric — verify it after creation
• get-environment — fetch the client-side SDK key when instrumenting

Optional MCP tools (enhance workflow):

• list-metrics — check for existing metrics with the same event key and understand naming conventions
• list-metric-events — discover which event keys have recent activity before committing to one (custom metrics only)

Two Different "Projects" — Never Confuse Them

Users work with two completely separate things that both get called "project." You must keep these distinct at all times:

Rules for resolving these from user input:

• If the user says *"my application at X"* or *"my codebase"* or *"my repo"* → they mean the local codebase. X is a folder path or project name, not a LaunchDarkly key.
• If the user says *"add it to X"* or *"in LaunchDarkly"* or *"my LD project"* → they mean the LaunchDarkly project. X is the projectKey for API calls.
• A user can name their local codebase checkout_proj while their LaunchDarkly project is anthony-agent-dev-5000. These are unrelated.
• Never assume the local codebase name is a LaunchDarkly project key. If you're unsure which is which, ask directly: *"Just to confirm — what's your LaunchDarkly project key? (This is different from your local app name — you can find it in the LD UI under Account Settings > Projects.)"*

When both are needed (e.g. for a custom metric with instrumentation), confirm each explicitly before proceeding.

Workflow

Step 1: Determine the Metric Kind

LaunchDarkly has three metric kinds. Choose the right one before anything else.

Decision rules:

• User says "track when someone views a page / visits a URL" → pageview (preferred — no instrumentation required)
• User says "track when someone clicks a button / link" → click
• User says "track a custom event" or references a track() call → custom

When pageview or click would work, suggest it over custom — it requires no code changes.

Step 2: Resolve the Data Source

For pageview and click metrics:

• Ask for the URL(s) to match. Confirm the kind of URL match rule:

- substring — URL contains this string (most common) - exact — URL must match exactly - canonical — matches the canonical URL - regex — full regex pattern

• For click metrics, also ask for the CSS selector (e.g. .checkout-btn, #submit).
• Skip list-metric-events — these metrics don't use event keys.
• Skip to Step 3.

For custom metrics — check events first, instrument if needed:

Call list-metric-events immediately to see which event keys are already flowing:

list-metric-events(projectKey, environmentKey?)

Case A — the event key is already in the list: Confirm the key with the user and proceed to Step 3. No instrumentation needed.

Case B — the event key is NOT in the list: The metric can't measure anything without events. Instrument the event now before creating the metric. Do not simply warn and ask whether to proceed — treat instrumentation as the default next action.

Follow the instrumentation sub-workflow below, then re-check list-metric-events to confirm events are flowing before moving to Step 3. Only skip instrumentation if the user explicitly says they want to create the metric first and wire the event up later — in that case, remind them at the end that the metric will produce no data until the event is tracked.

Step 2b: Instrument the Event (when events aren't flowing)

This sub-workflow gets a track() call into the codebase and connects the app to the right LaunchDarkly environment. Complete all steps before returning to the main workflow.

1. Find the right place in the codebase. Locate the function or handler where the event naturally occurs (e.g. a checkout submit handler, a form submission callback). Read the relevant source files to understand the existing structure before making changes.

2. Determine the event key. If the user hasn't specified one, propose a descriptive kebab-case key that matches what the code is doing (e.g. checkout-completed, signup-submitted). Confirm with the user before using it.

3. Fetch the client-side SDK key. Ask the user which environment they want to connect to (e.g. "test", "production", "staging") — just the environment name. Then call:

get-environment(projectKey, environmentKey)

Use the clientSideId from the response.

4. Write the environment file. Check whether a .env file (or equivalent — .env.local, .env.development, etc.) already exists.

• If the file does not exist, create it.
• If the file exists and already contains the key (e.g. VITE_LD_CLIENT_SIDE_ID), compare the stored value to the clientSideId returned by get-environment. If they differ, surface the discrepancy to the user: "Your .env already has VITE_LD_CLIENT_SIDE_ID=<old>, but get-environment returned <new> for the <env> environment. Should I update it?" Do not silently keep the old value — a mismatched client-side ID means events will be sent to the wrong project or environment.
• If the file exists but the key is absent, add it without touching other values.

Use the variable name appropriate to the project's build tool (e.g. VITE_LD_CLIENT_SIDE_ID for Vite, REACT_APP_LD_CLIENT_SIDE_ID for CRA, NEXT_PUBLIC_LD_CLIENT_SIDE_ID for Next.js).

4b. Set the SDK base URL if the user is not on app.launchdarkly.com. The SDK defaults to app.launchdarkly.com for all traffic. If the user is on a different LaunchDarkly deployment (e.g. an internal staging environment like catamorphic, or a dedicated instance), events and flag evaluations will silently go to the wrong host.

Detect this by inspecting any _links or UI URLs in MCP API responses — if they point to a host other than app.launchdarkly.com, you are on a non-production deployment. When in doubt, ask:

"Are you connecting to app.launchdarkly.com or a different LaunchDarkly instance? (e.g. an internal or staging environment)"

If they are on a non-standard host, add three additional variables to the .env file:

VITE_LD_BASE_URL=https://<their-host>
VITE_LD_STREAM_URL=https://clientstream.<their-host-domain>
VITE_LD_EVENTS_URL=https://events.<their-host-domain>

And pass them to the SDK options at init time:

asyncWithLDProvider({
  clientSideID,
  context: { kind: 'user', anonymous: true },
  options: {
    baseUrl: import.meta.env.VITE_LD_BASE_URL,
    streamUrl: import.meta.env.VITE_LD_STREAM_URL,
    eventsUrl: import.meta.env.VITE_LD_EVENTS_URL,
  },
})

Omit the options block entirely if they are on app.launchdarkly.com — the defaults are correct and no extra config is needed.

5. Install and initialize the SDK if it isn't already present. Check package.json (or the equivalent dependency file) for an existing LD SDK. If none is found, install the right one for the project's stack:

• React → launchdarkly-react-client-sdk
• Browser JS → launchdarkly-js-client-sdk
• Node.js server → @launchdarkly/node-server-sdk

Initialize the SDK at the app's entry point (e.g. wrap the React root with LDProvider, configure LDClient.init() in the server entry, etc.). Pass the client-side ID from the env file. Use an anonymous user/context as the default unless the app already manages user context.

6. Add the track() call. In the location identified in step 1, add the call immediately before or after the action completes:

• Count / occurrence metric: ldClient.track('event-key')
• Value metric: ldClient.track('event-key', null, numericValue)

Use optional chaining (ldClient?.track(...)) in client-side code where the client may not yet be initialized.

7. Verify events are flowing. After the instrumentation changes are made, remind the user to run the app and trigger the event at least once. Then call list-metric-events again to confirm the key appears before proceeding to metric creation.

Step 3: Check for Existing Metrics

Before creating anything, use list-metrics to scan the project:

1. Check for duplicates. Search for metrics with the same event key, URL pattern, or similar names. Avoid creating a second metric that measures the same thing — instead, flag the existing metric and ask the user if they want to reuse it.
2. Learn the naming convention. Are metric keys kebab-case or snake_case? Are there common tag patterns? Match what already exists.
3. Understand the tag taxonomy. Tags like team:growth, area:checkout, or type:guardrail may already exist. Suggest relevant tags based on what the user describes.

Step 4: Propose the Metric Configuration

Before calling any API, surface a proposed configuration in plain language for the user to confirm or edit.

Determine measure type. The right choice depends on what the user is trying to learn and how they'll use the metric — in an experiment, a guarded rollout, or a release policy. Do not assume. When the event is something a user can do repeatedly (click, add to cart, view page, etc.), always ask before proposing:

"Are you trying to measure how many times this event happens in total (count), or what percentage of users triggered it at least once (occurrence)?"

Tie the question to their context:

• Experiments — occurrence is common for conversion goals (did the treatment cause more users to do X?); count is better for engagement or volume goals (did the treatment cause more total actions?)
• Guarded rollouts / release policies — occurrence is typical for error rate guardrails (what fraction of users hit an error?); count suits absolute volume guardrails (total error events)
• If the user explicitly says "percent of users" or "conversion rate" → occurrence
• If the user explicitly says "number of times" or "total events" → count

Only skip asking if the intent is unambiguous from context (e.g. "API latency" → value, "error rate" → count, "signup conversion" → occurrence).

Determine success criteria:

• Higher is better → HigherThanBaseline (conversion rate, revenue, engagement)
• Lower is better → LowerThanBaseline (latency, error rate, bounce rate)

Use common templates as defaults when the user's intent is clear:

Present the proposed config before creating — don't silently fire the API:

Proposed metric:
  Key:              checkout-page-viewed
  Name:             Checkout Page Viewed
  Kind:             pageview (fires automatically on URL visit — no code change needed)
  URLs:             substring match on "/checkout"
  Measure type:     occurrence (did each user visit the page?)
  Success criteria: HigherThanBaseline

Proceed, or would you like to change anything?

STOP HERE. Do not call any API. Do not proceed to Step 5. Wait for the user to explicitly confirm before doing anything else. The user must respond with an approval (e.g. "yes", "looks good", "proceed") before you call create-metric. If there is any ambiguity in the proposed config — such as a choice between sum vs average, or the event key name — ask that question as part of the proposal and wait for the answer before continuing.

Step 5: Create the Metric

Only proceed once the user has explicitly confirmed the proposed config in Step 4. If you have not yet received a confirmation, go back and wait.

Once the user confirms, call create-metric. The tool handles the translation from measureType to the underlying API fields — you never need to pass isNumeric or unitAggregationType directly.

create-metric(
  projectKey,
  key,
  name,
  kind,              // "custom" | "pageview" | "click"
  eventKey?,         // only for kind="custom"
  urls?,             // only for kind="pageview" or "click": [{ kind, url }]
  selector?,         // only for kind="click": CSS selector string
  measureType,       // "count" | "occurrence" | "value"
  successCriteria,   // "HigherThanBaseline" | "LowerThanBaseline"
  valueAggregation?, // only for measureType="value": "average" (default) or "sum"
  unit?,             // display label: "ms", "USD", etc.
  description?,
  tags?
)

Step 6: Verify

Use get-metric to confirm the metric was created with the right configuration:

1. Key and name match what was requested.
2. kind is correct — custom, pageview, or click.
3. measureType is correct — double-check by reading back the measureType field, not just isNumeric.
4. eventKey / urls / selector are set to the intended values.
5. successCriteria is correct.

Surface a summary to the user:

✓ Metric created: checkout-page-viewed
  Kind:     pageview (auto-tracked on URL visit)
  URLs:     substring "/checkout"
  Measures: occurrence (conversion rate)
  Goal:     Higher is better

View in LaunchDarkly: {_links.ui from the create-metric response}

The create-metric tool returns a _links.ui field with the correct URL for the environment being used. Always use that value — never hard-code app.launchdarkly.com.

Measure Type Reference

The create-metric tool translates measureType to the LD API fields internally. You never need to set isNumeric or unitAggregationType directly.

For value metrics, valueAggregation defaults to "average". Pass valueAggregation: "sum" for revenue or cumulative totals.

Important Context

• Prefer pageview and click over custom when possible. They require no SDK instrumentation and work automatically in browser environments.
• Event keys are case-sensitive. checkout-completed and Checkout-Completed are different events. Match the key exactly as it appears in your track() calls.
• Custom metrics without events produce no data. A custom metric is only useful once its event key is actively being tracked in production (or the relevant environment). If you created the metric before instrumenting the event, remind the user.
• Metric keys are immutable. Once created, a metric's key cannot be changed. Choose carefully.
• Metrics are project-scoped. A metric created in one project is not visible in another. Make sure projectKey matches where the experiment or flag lives.
• One primary metric per experiment. When attaching this metric to an experiment, clarify whether it's the primary metric (the one that determines success or failure) or a secondary metric (a guardrail or supporting signal). See the LaunchDarkly docs for experiment setup.